janhq/Jan-code-4b-gguf
Text Generation • 4B • Updated
• 14
Jan Team
jan-ai
AI & ML interests
Also at https://huggingface.co/janhq
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a model about 12 hours ago
janhq/Jan-code-4b-gguf updated
a model about 12 hours ago
janhq/Jan-code-4b-gguf published
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janhq/Jan-code-4b Organizations
published a model about 12 hours ago
updated a model about 12 hours ago
published a model about 13 hours ago
updated a model about 15 hours ago
reacted to alandao's post with 🔥 about 15 hours ago
Post
1414
Don’t give up 🔥
Do you know what I was planning to do this time last week?
I was preparing to write a report declaring that Jan Nano was a failed project because the benchmark results didn’t meet expectations.
But I thought — it can’t be. When loading the model into the app, the performance clearly felt better. So why were the benchmark results worse?
That’s when I reviewed the entire benchmark codebase and realized something fundamental: agentic or workflow-based approaches introduce a huge gap and variation when benchmarking. Jan-nano was trained with an agentic setup — it simply can’t be benchmarked using a rigid workflow-based method.
I made the necessary changes, and the model ended up performing even better than before the issues arose. Turns out the previous benchmarking method conflicted with the way the model was trained.
What if I had given up? That would’ve meant 1.5 months of training and a huge amount of company resources wasted.
But now, this is officially the most successful and biggest release for the whole team — all thanks to Jan-nano.
Menlo/Jan-nano
Do you know what I was planning to do this time last week?
I was preparing to write a report declaring that Jan Nano was a failed project because the benchmark results didn’t meet expectations.
But I thought — it can’t be. When loading the model into the app, the performance clearly felt better. So why were the benchmark results worse?
That’s when I reviewed the entire benchmark codebase and realized something fundamental: agentic or workflow-based approaches introduce a huge gap and variation when benchmarking. Jan-nano was trained with an agentic setup — it simply can’t be benchmarked using a rigid workflow-based method.
I made the necessary changes, and the model ended up performing even better than before the issues arose. Turns out the previous benchmarking method conflicted with the way the model was trained.
What if I had given up? That would’ve meant 1.5 months of training and a huge amount of company resources wasted.
But now, this is officially the most successful and biggest release for the whole team — all thanks to Jan-nano.
Menlo/Jan-nano
updated 2 models 18 days ago
Update README.md
#3 opened about 1 month ago
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jan-ai
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published 2 models 9 months ago
updated a model 9 months ago
published a model 9 months ago
updated a model 9 months ago
published 2 models 9 months ago